The really important job interview questions enginers should ask (but don't)
https://posthog.com/blog/what-to-ask-in-interviews Disclaimer: These questions are direct, but a company that reacts badly to them may not be a good place to work. There are also a lot of questions here - think of them as themes, and you don’t need to ask them all. Prioritize based on what you hear...
Clean architecture: How to structure your ML projects to reduce technical debt
https://laszlo.substack.com/p/slides-for-my-talk-at-pydata-london Youtube: https://www.youtube.com/watch?v=QXfsS-ZOeyA Quick summary of slides: What do we mean by “ML products”? Why does tech debt matter in ML? How ML Lifecycle affects tech debt? Tech Debt vs Tech Mess (This slide was received by a significant amount of laughter) What is refactoring? What is Experimental-Operational Symmetry (EOS)?...
Flat teams drive scientific innovation
https://www.pnas.org/doi/10.1073/pnas.2200927119 With teams growing in all areas of scientific and scholarly research, we explore the relationship between team structure and the character of knowledge they produce. Drawing on 89,575 self-reports of team member research activity underlying scientific publications, we show how individual activities cohere into broad roles of 1) leadership...
How should analysts spend their time
https://mikkeldengsoe.substack.com/p/time-allocation Gitlab data-team handbook: https://about.gitlab.com/handbook/business-technology/data-team/ However, one common topic is that data people spend upwards 50% of their time working reactively, often dealing with data issues or trying to find or get access to data. Examples of this are: A stakeholder mentions that a KPI in a dashboard looks different...
Pruning recurrent neural networks replicates adolescent changes in working memory and reinforcement learning
https://www.pnas.org/doi/10.1073/pnas.2121331119 https://github.com/baverbeck/Pruning-RNNs Adolescent development is characterized by an improvement in multiple cognitive processes. While performance on cognitive operations improves during this period, the ability to learn new skills quickly, for example, a new language, decreases. During this time, there is substantial pruning of excitatory synapses in cortex and specifically in...
Ready to learn: Incidental exposure fosters category learning
https://journals.sagepub.com/doi/10.1177/09567976211061470 Our knowledge of the world is populated with categories such as dogs, cups, and chairs. Such categories shape how we perceive, remember, and reason about their members. Much of our exposure to the entities we come to categorize occurs incidentally as we experience and interact with them in our...
The Batch: Special issue! Foundational algorithms, where they came from, where they're going
https://info.deeplearning.ai/the-batch-special-issue-foundational-algorithms-where-they-came-from-where-theyre-going In that spirit, this week’s issue of The Batch explores some of our field’s most important algorithms, explaining how they work and describing some of their surprising origins. If you’re just starting out, I hope it will demystify some of the approaches at the heart of machine learning. For...
What I wish I knew about onboarding effectively
https://eugeneyan.com/writing/onboarding/ Starting with the right mindset Own your onboarding. How successful your onboarding is lies with you. While your manager may have an initial onboarding plan, you own it and should feel empowered to update it as necessary. Treat the onboarding like any other project you’re tasked to lead. Taking...
The top 5 scale-up mistakes
https://kellblog.com/2022/05/23/preview-of-my-saastr-europa-talk-the-top-5-scale-up-mistakes/ Slides: https://kellblog.com/2022/06/07/saastr-europa-slides-the-top-5-mistakes-in-scale-up/ I’d move to scale-up, and specifically the things that can go wrong as you scale a company from $10M to $100M in ARR. Even if your company is still below $10M, I think you’ll enjoy the presentation because it will provide you with a preview of what...
Chronic frames of social inequality: How mainstream media frame race, gender, and wealth inequality
https://www.pnas.org/doi/10.1073/pnas.2110712119 How social inequality is described—as advantage or disadvantage—critically shapes individuals’ responses to it [e.g., B. S. Lowery, R. M. Chow, J. R. Crosby, J. Exp. Soc. Psychol. 45, 375–378, 2009]. As such, it is important to document how people, in fact, choose to describe inequality. In a corpus of...
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a collection of excerpts from interesting research, blogs, events, etc...